{"id":"W3087412484","doi":"10.1101/2020.09.18.292680","title":"LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Triangulation; Artificial intelligence; Computer vision; Computer science; Pose; Calibration; Kinematics; Camera resectioning; Deep learning; Macaque; Single camera; Mathematics; Geography; Psychology; Cartography; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001119577,0.0008735304,0.001015773,0.0007617148,0.0002784467,0.000615291,0.002113798,0.0004558653,0.00000897368],"category_scores_gemma":[0.0006995985,0.0009976072,0.0002622559,0.001995404,0.00007749612,0.0005546293,0.0007544916,0.001428269,0.00004109664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004156658,"about_ca_system_score_gemma":0.0009959579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001510897,"about_ca_topic_score_gemma":0.000002489216,"domain_scores_codex":[0.9947609,0.0002609901,0.0009472949,0.002286917,0.0006227524,0.001121117],"domain_scores_gemma":[0.9966293,0.0001900049,0.0004176471,0.001434493,0.0006172573,0.0007112778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004249359,0.0008681045,0.003319852,0.003209084,0.0001926825,0.0002847132,0.0006297109,0.06220091,0.9228143,0.005010159,0.0003075092,0.0007380235],"study_design_scores_gemma":[0.002248024,0.0003937712,0.004648709,0.000837906,0.00006243166,3.34789e-8,0.00002247,0.8072848,0.1557212,0.00001528699,0.02635023,0.002415059],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0142723,0.001025462,0.9797834,0.001009066,0.0006405814,0.002152379,0.00008992894,0.001009631,0.00001723219],"genre_scores_gemma":[0.561697,0.00003266221,0.436026,0.00135097,0.0002237163,0.0005394495,5.466305e-7,0.000128222,0.000001382409],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7670931,"threshold_uncertainty_score":0.9992474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01812446200043053,"score_gpt":0.2478394158025659,"score_spread":0.2297149538021354,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}